Issue |
Int. J. Simul. Multidisci. Des. Optim.
Volume 15, 2024
|
|
---|---|---|
Article Number | 9 | |
Number of page(s) | 9 | |
DOI | https://doi.org/10.1051/smdo/2024004 | |
Published online | 12 April 2024 |
Research Article
Application of 3D recognition algorithm based on spatio-temporal graph convolutional network in basketball pose estimation
Graduate School, University of Perpetual Help System DALTA, Manila City 1740, Philippines
* e-mail: ymingzhi@outlook.com
Received:
21
August
2023
Accepted:
18
March
2024
In recent years, human motion recognition in computer vision has become a hot research direction in this field. Based on 2D human motion recognition technology, real-time extraction of motion features from 2D planes is used to recognize human movements. This method can only learn the position contour and color information of the image. It cannot directly reflect the motion situation, which results in low recognition accuracy and efficiency. In response to this issue, this study proposes a combination method of motion recognition and 3D pose estimation to recognize and classify basketball movements. First, the 2D skeleton model is obtained by extracting the feature information in the video action, which is converted into a 3D model, and the model is replaced by the time-space convolutional network to establish a human action recognition model. The experiment showed that when the number of iterations reached 6, the accuracy of the spatio-temporal graph convolutional network algorithm model reached 92%. Comparing the accuracy rates of different algorithm models, the average accuracy rates of convolutional neural network, long short memory network, graph convolution, long short model of action recognition and graph convolution model of action recognition were 61.6%, 65.4%, 72.5%, 76.8% and 90.3% respectively. The results show that the proposed 3D recognition algorithm can accurately recognize different basketball movements. This study can provide reference for basketball coaches and athletes in basketball training.
Key words: Spatio-temporal graph / CNN / video analysis / action recognition / attitude estimation / skeleton model / AI / modelling
© M. Ye, Published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.